license: apache-2.0
pipeline_tag: image-segmentation
tags:
- image-segmentation
- historical-maps
- cartography
- vectorization
library_name: pytorch
Building Block Vectorization in Historical Maps
Polygon extraction of building blocks from historical maps of Paris. Trained as part of the course Research Topics in Cartography at ETH Zurich (Spring 2026). Source code: github.com/dav1dclara/cartography-research.
Models
Single-class binary segmentation of building blocks ("block"). Each subfolder contains one model_f{N}.safetensors per cross-validation fold plus a shared config.json carrying model configuration, class names, per-fold and ensemble decision thresholds, normalization mode, recommended sliding-window patch size (512), and number of folds.
| Subfolder | Architecture | Encoder | Folds |
|---|---|---|---|
unet_scse |
U-Net + SCSE attention | EfficientNet-B3 | 2 |
Usage
pip install torch segmentation-models-pytorch safetensors huggingface_hub pillow numpy
python inference.py \
--hf-repo davidclara/building-block-vectorization \
--model-name unet_scse \
--image map.jpg \
--out mask.png
inference.py is a minimal example: Gaussian-weighted sliding-window prediction with stride = patch_size // 2, normalization driven by config.json (simple = /255, imagenet = mean/std), sigmoid averaged across folds per patch, thresholding with the ensemble threshold, and writing a single binary PNG mask. For the full training and inference pipeline see the GitHub repository.